NDLI: Genetic Optimization of Fuzzy Polynomial Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Seok-Beom Roh Pedrycz, W. Sung-Kwun Oh
Copyright Year	1982
Abstract	In this paper, we introduce a new topology of fuzzy-neural networks-fuzzy-set-based polynomial neural networks (FSPNNs). The two underlying design mechanisms of such networks involve genetic optimization and information granulation (IG). The resulting constructs come in the form of fuzzy polynomial neural networks with fuzzy-set-based polynomial neurons, regarded as their generic processing elements. First, we introduce a comprehensive design methodology using which we determine the optimal structure of the FSPNNs. This methodology hinges on the extended group method of data handling and fuzzy-set-based rules. It concerns the optimization of the FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit a notion of information granules defined over a system's variables and formed through the process of IG. This granulation is realized with the aid of the hard C-means clustering algorithm. The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.
Sponsorship	IEEE Industrial Electronics Society
Starting Page	2219
Ending Page	2238
Page Count	20
File Size	3246009
File Format	PDF
ISSN	02780046
Volume Number	54
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetics Fuzzy neural networks Polynomials Neural networks Input variables Network topology Design optimization Neurons Design methodology Fasteners information granulation (IG) Fuzzy-set-based polynomial neural networks (FSPNNs) fuzzy-set-based polynomial neuron (FSPN) genetic algorithms (GAs) group method of data handling (GMDH) hard C-means (HCM) clustering
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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